code: def train_one_epoch(
      model,
      optimizer,
      data_loader,
      device,
      epoch,
      print_freq,
      accumulate,
      img_size,
      grid_min,
      grid_max,
      gs,
      multi_scale=False,
      warmup=False,
      scaler=None,
  ):
      model.train()
      metric_logger = utils.MetricLogger(delimiter="  ")
      metric_logger.add_meter(
          "lr", utils.SmoothedValue(window_size=1, fmt="{value:.6f}")
      )
      header = "Epoch: [{}]".format(epoch)
  
      lr_scheduler = None
      if (
          epoch == 0 and warmup is True
      ):  # 当训练第一轮（epoch=0）时，启用warmup训练方式，可理解为热身训练
          warmup_factor = 1.0 / 1000
          warmup_iters = min(1000, len(data_loader) - 1)
  
          lr_scheduler = utils.warmup_lr_scheduler(
              optimizer, warmup_iters, warmup_factor
          )
          accumulate = 1
  
      mloss = torch.zeros(4).to(device)  # mean losses
      now_lr = 0.0
      nb = len(data_loader)  # number of batches
      # imgs: [batch_size, 3, img_size, img_size]
      # targets: [num_obj, 6] , that number 6 means -> (img_index, obj_index, x, y, w, h)
      # paths: list of img path
      for i, (imgs, targets, paths, _, _) in enumerate(
          metric_logger.log_every(data_loader, print_freq, header)
      ):
          # ni 统计从epoch0开始的所有batch数
          ni = (
              i + nb * epoch
          )  # number integrated batches (since train start)
          imgs = (
              imgs.to(device).float() / 255.0
          )  # uint8 to float32, 0 - 255 to 0.0 - 1.0
          targets = targets.to(device)
  
          # Multi-Scale
          if multi_scale:
              # 每训练64张图片，就随机修改一次输入图片大小，
              # 由于label已转为相对坐标，故缩放图片不影响label的值
              if (
                  ni % accumulate == 0
              ):  # adjust img_size (67% - 150%) every 1 batch
                  # 在给定最大最小输入尺寸范围内随机选取一个size(size为32的整数倍)
                  img_size = (
                      random.randrange(grid_min, grid_max + 1) * gs
                  )
              sf = img_size / max(imgs.shape[2:])  # scale factor
  
              # 如果图片最大边长不等于img_size, 则缩放图片，并将长和宽调整到32的整数倍
              if sf != 1:
                  # gs: (pixels) grid size
                  ns = [
                      math.ceil(x * sf / gs) * gs
                      for x in imgs.shape[2:]
                  ]  # new shape (stretched to 32-multiple)
                  imgs = F.interpolate(
                      imgs,
                      size=ns,
                      mode="bilinear",
                      align_corners=False,
                  )
  
          # 混合精度训练上下文管理器，如果在CPU环境中不起任何作用
          with amp.autocast(enabled=scaler is not None):
              pred = model(imgs)
  
              # loss
              loss_dict = compute_loss(pred, targets, model)
              losses = sum(loss for loss in loss_dict.values())
  
          # reduce losses over all GPUs for logging purpose
          loss_dict_reduced = utils.reduce_dict(loss_dict)
          losses_reduced = sum(
              loss for loss in loss_dict_reduced.values()
          )
          loss_items = torch.cat(
              (
                  loss_dict_reduced["box_loss"],
                  loss_dict_reduced["obj_loss"],
                  loss_dict_reduced["class_loss"],
                  losses_reduced,
              )
          ).detach()
          mloss = (mloss * i + loss_items) / (
              i + 1
          )  # update mean losses
  
          if not torch.isfinite(losses_reduced):
              print(
                  "WARNING: non-finite loss, ending training ",
                  loss_dict_reduced,
              )
              print("training image path: {}".format(",".join(paths)))
              sys.exit(1)
  
          losses *= 1.0 / accumulate  # scale loss
  
          # backward
          if scaler is not None:
              scaler.scale(losses).backward()
          else:
              losses.backward()
  
          # optimize
          # 每训练64张图片更新一次权重
          if ni % accumulate == 0:
              if scaler is not None:
                  scaler.step(optimizer)
                  scaler.update()
              else:
                  optimizer.step()
              optimizer.zero_grad()
  
          metric_logger.update(loss=losses_reduced, **loss_dict_reduced)
          now_lr = optimizer.param_groups[0]["lr"]
          metric_logger.update(lr=now_lr)
  
          if (
              ni % accumulate == 0 and lr_scheduler is not None
          ):  # 第一轮使用warmup训练方式
              lr_scheduler.step()
  
      return mloss, now_lr
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/yolov3_spp/train_utils/train_eval_utils.py
imports: import sys
  from torch.cuda import amp
  import torch.nn.functional
  from build_utils.utils import *
  from .coco_eval import CocoEvaluator
  from .coco_utils import get_coco_api_from_dataset
  import train_utils.distributed_utils
label_desc: Use image node_id 1 for calling a global function with example usage: train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq, accumulate, img_size, grid_min, grid_max, gs, multi_scale, warmup, scaler) and returns: mloss,  now_lr